TY - JOUR
T1 - Optimizing diamond-like carbon coatings - From experimental era to artificial intelligence
AU - Zia, Abdul Wasy
AU - Hussain, Syed Asad
AU - Baig, Mirza Muhammad Faran Ashraf
PY - 2022/12/15
Y1 - 2022/12/15
N2 - Diamond-like carbon (DLC) coatings are widely used for numerous engineering applications due to their superior multi-functional properties. Deposition of good quality DLC is governed by energy per unit carbon atom or ion and plasma kinetics, which are independent parameters. Translating independent parameters to dependent parameters to produce a best DLC is subjected to deposition method, technology, and system configurations which may involve above 50 combinations of bias voltage, chamber pressure, deposition temperature, gas flow rate, etc. Hence DLC coatings are optimized to identify the best parameters which yield superior properties. This article covers DLC introduction, the role of independent parameters, translation of independent parameters to dependent parameters, and discussion of four generations of DLC optimization. The first-generation of DLC optimization experimentally optimizes the parameter-to-property relationship, and the second-generation describes multi-parameter optimization with a hybrid of experimental and statistical-based analytical methods. The third generation covers the optimization of DLC deposition parameters with a hybrid of statistical methods and artificial intelligence (AI) tools. The ongoing fourth generation not only performs multi-parameter and multi-property optimization but also use AI tools to predict DLC properties and performance with higher accuracy. It is expected that AI-driven DLC optimizations and progress in virtual synthesis of DLC will not only assist in resolving DLC challenges specific to emerging markets and complex environments, but will also become a pathway for DLC to enter a digital-twin era.
AB - Diamond-like carbon (DLC) coatings are widely used for numerous engineering applications due to their superior multi-functional properties. Deposition of good quality DLC is governed by energy per unit carbon atom or ion and plasma kinetics, which are independent parameters. Translating independent parameters to dependent parameters to produce a best DLC is subjected to deposition method, technology, and system configurations which may involve above 50 combinations of bias voltage, chamber pressure, deposition temperature, gas flow rate, etc. Hence DLC coatings are optimized to identify the best parameters which yield superior properties. This article covers DLC introduction, the role of independent parameters, translation of independent parameters to dependent parameters, and discussion of four generations of DLC optimization. The first-generation of DLC optimization experimentally optimizes the parameter-to-property relationship, and the second-generation describes multi-parameter optimization with a hybrid of experimental and statistical-based analytical methods. The third generation covers the optimization of DLC deposition parameters with a hybrid of statistical methods and artificial intelligence (AI) tools. The ongoing fourth generation not only performs multi-parameter and multi-property optimization but also use AI tools to predict DLC properties and performance with higher accuracy. It is expected that AI-driven DLC optimizations and progress in virtual synthesis of DLC will not only assist in resolving DLC challenges specific to emerging markets and complex environments, but will also become a pathway for DLC to enter a digital-twin era.
KW - Artificial intelligence
KW - Deposition
KW - Diamond-like carbon
KW - Optimization
KW - Performance
KW - Properties
UR - http://www.scopus.com/inward/record.url?scp=85140069037&partnerID=8YFLogxK
U2 - 10.1016/j.ceramint.2022.10.149
DO - 10.1016/j.ceramint.2022.10.149
M3 - Review article
VL - 48
SP - 36000
EP - 36011
JO - Ceramics International
JF - Ceramics International
SN - 0272-8842
IS - 24
ER -